# A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter

arXiv: https://arxiv.org/abs/2206.14619 · HTML: https://mlsanity.com/paper/2206.14619/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 27 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [A Sentiment Analysis Dataset for Code-Mixed Malayalam-English](https://mlsanity.com/paper/2006.00210/) (2020)
- [GottBERT: a pure German Language Model](https://mlsanity.com/paper/2012.02110/) (2020)
- [Detection of Novel Social Bots by Ensembles of Specialized Classifiers](https://mlsanity.com/paper/2006.06867/) (2020)
- [Demographic Inference and Representative Population Estimates from Multilingual Social Media Data](https://mlsanity.com/paper/1905.05961/) (2019)
- [MMCoVaR: Multimodal COVID-19 Vaccine Focused Data Repository for Fake News Detection and a Baseline Architecture for Classification](https://mlsanity.com/paper/2109.06416/) (2021)
- [Polarization of the Vaccination Debate on Facebook](https://mlsanity.com/paper/1801.02903/) (2018)
- [VaccinItaly: monitoring Italian conversations around vaccines on Twitter and Facebook](https://mlsanity.com/paper/2101.03757/) (2021)

## Built on by (most cited first, 5 of 5)

- [VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter](https://mlsanity.com/paper/2301.06660/) (2023)
- [Examining Temporalities on Stance Detection towards COVID-19 Vaccination](https://mlsanity.com/paper/2304.04806/) (2023)
- [Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2410.03293/) (2024)
- [UstanceBR: a social media language resource for stance prediction](https://mlsanity.com/paper/2312.06374/) (2023)
- [Clarifying Misconceptions in COVID-19 Vaccine Sentiment and Stance Analysis and Their Implications for Vaccine Hesitancy Mitigation: A Systematic Review](https://mlsanity.com/paper/2503.18095/) (2025)

## Abstract (arXiv)

Vaccine hesitancy is considered as one main cause of the stagnant uptake ratio of COVID-19 vaccines in Europe and the US where vaccines are sufficiently supplied. Fast and accurate grasp of public attitudes toward vaccination is critical to address vaccine hesitancy, and social media platforms have proved to be an effective source of public opinions. In this paper, we describe the collection and release of a dataset of tweets related to COVID-19 vaccines. This dataset consists of the IDs of 2,198,090 tweets collected from Western Europe, 17,934 of which are annotated with the originators' vaccination stances. Our annotation will facilitate using and developing data-driven models to extract vaccination attitudes from social media posts and thus further confirm the power of social media in public health surveillance. To lay the groundwork for future research, we not only perform statistical analysis and visualisation of our dataset, but also evaluate and compare the performance of established text-based benchmarks in vaccination stance extraction. We demonstrate one potential use of our data in practice in tracking the temporal changes of public COVID-19 vaccination attitudes.